Fine tuning a machine learning model is to improve the accuracy of the results that are forecasted.
Steps involved in fine-tuning are:
- After getting to know more about machine learning and about tuning in detail, you should then determine the metric that you are going to use to record the accuracy of the model.
- Test the accuracy of the model after you set the required accuracy metric by using cross-validation methodologies.
- Once you are set with the accuracy, then determine the parameters that your machine learning model requires with the help of the validation curve.
- Afterward, do a grid search to enhance the parameter condition.
- If you aren’t satisfied with the accuracy then keep on tuning it continuously.
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